Why sitting down and talking does not help - (and what it means for the data scientist)

What is the sane person's advise to two people who cannot agree on something? It is usually sit down and resolve their differences. A set of recent recent studies seem to suggest that it doesn't really help.

Some recent studies have shown that when people with strong opposing positions are put together to talk it out, it makes them even more entrenched in their opinions. This is the point put forward by Cass Sunstein in his book Going to Extremes. Even when the groups/ people with opposing views are presented objective evidence, people tend to "see" what they want to believe in the data and ignore the rest.

Another study cited by the Economist struck a similar message. The study was looking at self-help books which stress positive thinking, and their impacts on people. What the study found was that positive thinking only helps for people who are predisposed to thinking positively. The study can be credited to Joanne Wood of the University of Waterloo in Canada and her colleagues. The researchers report in Psychological Science journal that when people with high self-esteem are made to repeat positive reinforcing messages, they do tend to take more positive positions (on standardized tests) than people who do not repeat positive reinforcing messages.

So far so good. It sounds as though positive reinforcing helps. But when the test was done on people with low self-esteem, the results were quite the opposite. People who repeated the positive reinforcing message took less positive positions than the ones that did not repeat the message. So it seems to imply that positive reinforcement actually hurts when applied to people who are inclined to believe otherwise. For me, this sounds like another example of people entrenching towards their own biases. When people with entrenched positions are forced to take a contrary position (or look at objective data), they tend to entrench even further on their original positions.

So what are the implications for the data scientist from all of this?
Mostly that predisposed positions produce a sort of "blindness" to objective data. We all suffer from confirmation bias, we like to believe what we like to believe. It is therefore a great effort for us to actively look at data objectively and take what that data is telling us, vs. putting the appropriate spin that suits us. The data scientist needs to exercise tremendous discipline here. It takes a superhuman effort not to succumb to our biases and to (not) believe what we want to believe, and take a genuine interest in forming an objective opinion.

One of the bigger learning for me in all of this is also the importance of give-and-take in making progress on any issue. Because of the entrenchment bias, people seldom change their views (i.e., come around to your way of thinking) based on objective data and logical persuasion. They only come along when they have skin in the game and for that to happen, there has to be an active element of give-and-take between the two parties. Which makes me even more admiring of GOOD politicians and diplomats. Their ability to keep moving forward on an issue in a bipartisan manner comes out of their skill in give-and-take, and thereby overcoming the entrenchment bias.